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Recommendation systems are becoming important with the increased availability of online services. A typical approach used in recommendations is collaborative filtering. However, because it largely relies on external relations, such as items-to-items or users-to-users, problems occur when the relations are biased or insufficient. Focusing on that limitation, we here suggest a new method, item-network-based collaborative filtering, which recommends items through four steps. First, the system constructs item networks based on users’ item usage history and calculates three types of centrality: betweenness, closeness, and degree. Next, the system secures significant items based on the betweenness centrality of the items in each user's item network. Then, by using the closeness and degree centrality of the secured items, the algorithm predicts preference scores for items and their rank orders from each user's …